{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1.0\n",
      "1     3.0\n",
      "2     4.0\n",
      "3     NaN\n",
      "4    44.0\n",
      "5     1.0\n",
      "dtype: float64\n",
      "DatetimeIndex(['2017-10-09', '2017-10-10', '2017-10-11', '2017-10-12',\n",
      "               '2017-10-13', '2017-10-14'],\n",
      "              dtype='datetime64[ns]', freq='D')\n",
      "                   a         b         c         d\n",
      "2017-10-09  0.827857  0.151108  0.265419  0.792049\n",
      "2017-10-10  0.280616  0.719324  0.556846  0.194033\n",
      "2017-10-11  0.538966  0.044244  0.572865  0.081814\n",
      "2017-10-12  0.560581  0.893978  0.861914  0.077088\n",
      "2017-10-13  0.850714  0.729712  0.003048  0.308228\n",
      "2017-10-14  0.051035  0.708464  0.715050  0.705602\n",
      "   0  1   2   3\n",
      "0  0  1   2   3\n",
      "1  4  5   6   7\n",
      "2  8  9  10  11\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "s = pd.Series([1,3,4,np.nan,44,1])\n",
    "print(s)\n",
    "dates = pd.date_range('20171009',periods=6)\n",
    "print(dates)\n",
    "df = pd.DataFrame(np.random.rand(6,4),index = dates,columns=['a','b','c','d'])\n",
    "print(df)\n",
    "df1 = pd.DataFrame(np.arange(12).reshape(3,4))\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A          B    C  D      E    F\n",
      "0  1.0 2013-01-02  1.0  3   test  foo\n",
      "1  1.0 2013-01-02  1.0  3  train  foo\n",
      "2  1.0 2013-01-02  1.0  3   test  foo\n",
      "3  1.0 2013-01-02  1.0  3  train  foo\n",
      "A           float64\n",
      "B    datetime64[ns]\n",
      "C           float32\n",
      "D             int32\n",
      "E          category\n",
      "F            object\n",
      "dtype: object\n",
      "Int64Index([0, 1, 2, 3], dtype='int64')\n",
      "Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')\n",
      "[[1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']\n",
      " [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']\n",
      " [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']\n",
      " [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']]\n",
      "         A    C    D\n",
      "count  4.0  4.0  4.0\n",
      "mean   1.0  1.0  3.0\n",
      "std    0.0  0.0  0.0\n",
      "min    1.0  1.0  3.0\n",
      "25%    1.0  1.0  3.0\n",
      "50%    1.0  1.0  3.0\n",
      "75%    1.0  1.0  3.0\n",
      "max    1.0  1.0  3.0\n",
      "                     0                    1                    2  \\\n",
      "A                    1                    1                    1   \n",
      "B  2013-01-02 00:00:00  2013-01-02 00:00:00  2013-01-02 00:00:00   \n",
      "C                    1                    1                    1   \n",
      "D                    3                    3                    3   \n",
      "E                 test                train                 test   \n",
      "F                  foo                  foo                  foo   \n",
      "\n",
      "                     3  \n",
      "A                    1  \n",
      "B  2013-01-02 00:00:00  \n",
      "C                    1  \n",
      "D                    3  \n",
      "E                train  \n",
      "F                  foo  \n",
      "     F      E  D    C          B    A\n",
      "0  foo   test  3  1.0 2013-01-02  1.0\n",
      "1  foo  train  3  1.0 2013-01-02  1.0\n",
      "2  foo   test  3  1.0 2013-01-02  1.0\n",
      "3  foo  train  3  1.0 2013-01-02  1.0\n",
      "     A          B    C  D      E    F\n",
      "0  1.0 2013-01-02  1.0  3   test  foo\n",
      "1  1.0 2013-01-02  1.0  3  train  foo\n",
      "2  1.0 2013-01-02  1.0  3   test  foo\n",
      "3  1.0 2013-01-02  1.0  3  train  foo\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'A' : 1.,\n",
    "                    'B' : pd.Timestamp('20130102'),\n",
    "                    'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "                    'D' : np.array([3] * 4,dtype='int32'),\n",
    "                    'E' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "                    'F' : 'foo'})\n",
    "print(df2)\n",
    "print(df2.dtypes)\n",
    "print(df2.index)\n",
    "print(df2.columns)\n",
    "print(df2.values)\n",
    "print(df2.describe())\n",
    "print(df2.T)\n",
    "print(df2.sort_index(axis=1,ascending=False))\n",
    "print(df2.sort_values(by='B'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             A   B   C   D\n",
      "2017-10-10   0   1   2   3\n",
      "2017-10-11   4   5   6   7\n",
      "2017-10-12   8   9  10  11\n",
      "2017-10-13  12  13  14  15\n",
      "2017-10-14  16  17  18  19\n",
      "2017-10-15  20  21  22  23\n",
      "普通的筛选方式\n",
      "2017-10-10     0\n",
      "2017-10-11     4\n",
      "2017-10-12     8\n",
      "2017-10-13    12\n",
      "2017-10-14    16\n",
      "2017-10-15    20\n",
      "Freq: D, Name: A, dtype: int32 2017-10-10     0\n",
      "2017-10-11     4\n",
      "2017-10-12     8\n",
      "2017-10-13    12\n",
      "2017-10-14    16\n",
      "2017-10-15    20\n",
      "Freq: D, Name: A, dtype: int32\n",
      "            A  B   C   D\n",
      "2017-10-10  0  1   2   3\n",
      "2017-10-11  4  5   6   7\n",
      "2017-10-12  8  9  10  11             A  B   C   D\n",
      "2017-10-10  0  1   2   3\n",
      "2017-10-11  4  5   6   7\n",
      "2017-10-12  8  9  10  11\n",
      "基于loc的筛选\n",
      "A    0\n",
      "B    1\n",
      "C    2\n",
      "D    3\n",
      "Name: 2017-10-10 00:00:00, dtype: int32\n",
      "             A   C\n",
      "2017-10-10   0   2\n",
      "2017-10-11   4   6\n",
      "2017-10-12   8  10\n",
      "2017-10-13  12  14\n",
      "2017-10-14  16  18\n",
      "2017-10-15  20  22\n",
      "A    0\n",
      "C    2\n",
      "Name: 2017-10-10 00:00:00, dtype: int32\n",
      "基于iloc的筛选\n",
      "13\n",
      "             B   C\n",
      "2017-10-13  13  14\n",
      "2017-10-14  17  18\n",
      "             B   C\n",
      "2017-10-11   5   6\n",
      "2017-10-13  13  14\n",
      "2017-10-15  21  22\n",
      "基于条件筛选\n",
      "             A   B   C   D\n",
      "2017-10-12   8   9  10  11\n",
      "2017-10-13  12  13  14  15\n",
      "2017-10-14  16  17  18  19\n",
      "2017-10-15  20  21  22  23\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "dates = pd.date_range('20171010', periods=6)\n",
    "df = pd.DataFrame(np.arange(24).reshape((6,4)), index = dates, columns=['A','B','C','D'])\n",
    "print(df)\n",
    "#普通的筛选方式\n",
    "print(\"普通的筛选方式\")\n",
    "print(df['A'],df.A)\n",
    "print(df[0:3],df['20171010':'20171012'])\n",
    "#基于loc的筛选\n",
    "print(\"基于loc的筛选\")\n",
    "print(df.loc['20171010'])\n",
    "print(df.loc[:,['A','C']])\n",
    "print(df.loc['20171010',['A','C']])\n",
    "#基于iloc的筛选\n",
    "print(\"基于iloc的筛选\")\n",
    "print(df.iloc[3,1])\n",
    "print(df.iloc[3:5,1:3])\n",
    "print(df.iloc[[1,3,5,],1:3])\n",
    "#基于条件筛选\n",
    "print(\"基于条件筛选\")\n",
    "print(df[df.B>8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             A   B   C   D\n",
      "2017-10-10   0   1   2   3\n",
      "2017-10-11   4   5   6   7\n",
      "2017-10-12   8   9  10  11\n",
      "2017-10-13  12  13  14  15\n",
      "2017-10-14  16  17  18  19\n",
      "2017-10-15  20  21  22  23\n",
      "             A   B     C   D\n",
      "2017-10-10   0   1     2   3\n",
      "2017-10-11   4   5     6   7\n",
      "2017-10-12   8   9  1111  11\n",
      "2017-10-13  12  13    14  15\n",
      "2017-10-14  16  17    18  19\n",
      "2017-10-15  20  21    22  23\n",
      "             A   B     C   D\n",
      "2017-10-10   0   1     2   3\n",
      "2017-10-11   4   5     6   7\n",
      "2017-10-12   8   9  1111  11\n",
      "2017-10-13  12  13  2222  15\n",
      "2017-10-14  16  17    18  19\n",
      "2017-10-15  20  21    22  23\n",
      "             A  B     C   D\n",
      "2017-10-10   0  1     2   3\n",
      "2017-10-11   4  5     6   7\n",
      "2017-10-12   8  0  1111  11\n",
      "2017-10-13  12  0  2222  15\n",
      "2017-10-14  16  0    18  19\n",
      "2017-10-15  20  0    22  23\n",
      "             A  B     C   D  F\n",
      "2017-10-10   0  1     2   3  0\n",
      "2017-10-11   4  5     6   7  0\n",
      "2017-10-12   8  0  1111  11  0\n",
      "2017-10-13  12  0  2222  15  0\n",
      "2017-10-14  16  0    18  19  0\n",
      "2017-10-15  20  0    22  23  0\n",
      "             A  B     C   D  F  G\n",
      "2017-10-10   0  1     2   3  0  1\n",
      "2017-10-11   4  5     6   7  0  2\n",
      "2017-10-12   8  0  1111  11  0  3\n",
      "2017-10-13  12  0  2222  15  0  4\n",
      "2017-10-14  16  0    18  19  0  5\n",
      "2017-10-15  20  0    22  23  0  6\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "dates = pd.date_range('20171010',periods=6)\n",
    "df = pd.DataFrame(np.arange(24).reshape((6,4)), index = dates, columns=['A','B','C','D'])\n",
    "print(df)\n",
    "df.iloc[2,2] = 1111\n",
    "print(df)\n",
    "df.loc['20171013','C'] = 2222\n",
    "print(df)\n",
    "df.B[df.A>4] = 0\n",
    "print(df)\n",
    "df['F'] = 0\n",
    "print(df)\n",
    "df['G'] = pd.Series([1,2,3,4,5,6],index=pd.date_range('20171010',periods=6))\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             A     B     C   D\n",
      "2017-10-10   0   NaN   2.0   3\n",
      "2017-10-11   4   5.0   NaN   7\n",
      "2017-10-12   8   9.0  10.0  11\n",
      "2017-10-13  12  13.0  14.0  15\n",
      "2017-10-14  16  17.0  18.0  19\n",
      "2017-10-15  20  21.0  22.0  23\n",
      "             A     B     C   D\n",
      "2017-10-12   8   9.0  10.0  11\n",
      "2017-10-13  12  13.0  14.0  15\n",
      "2017-10-14  16  17.0  18.0  19\n",
      "2017-10-15  20  21.0  22.0  23\n",
      "             A       B       C   D\n",
      "2017-10-10   0  1111.0     2.0   3\n",
      "2017-10-11   4     5.0  1111.0   7\n",
      "2017-10-12   8     9.0    10.0  11\n",
      "2017-10-13  12    13.0    14.0  15\n",
      "2017-10-14  16    17.0    18.0  19\n",
      "2017-10-15  20    21.0    22.0  23\n",
      "                A      B      C      D\n",
      "2017-10-10  False   True  False  False\n",
      "2017-10-11  False  False   True  False\n",
      "2017-10-12  False  False  False  False\n",
      "2017-10-13  False  False  False  False\n",
      "2017-10-14  False  False  False  False\n",
      "2017-10-15  False  False  False  False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "dates = pd.date_range('20171010',periods=6)\n",
    "df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D'])\n",
    "df.iloc[0,1]=np.nan\n",
    "df.iloc[1,2]=np.nan\n",
    "print(df)\n",
    "print(df.dropna(axis=0,how='any'))\n",
    "print(df.fillna(value=1111))\n",
    "print(df.isnull())\n",
    "print(np.any(df.isnull())==True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Student ID  name   age  gender\n",
      "0         1100  Kelly   22  Female\n",
      "1         1101    Clo   21  Female\n",
      "2         1102  Tilly   22  Female\n",
      "3         1103   Tony   24    Male\n",
      "4         1104  David   20    Male\n",
      "5         1105  Catty   22  Female\n",
      "6         1106      M    3  Female\n",
      "7         1107      N   43    Male\n",
      "8         1108      A   13    Male\n",
      "9         1109      S   12    Male\n",
      "10        1110  David   33    Male\n",
      "11        1111     Dw    3  Female\n",
      "12        1112      Q   23    Male\n",
      "13        1113      W   21  Female\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('student.csv')\n",
    "print(data)\n",
    "data.to_pickle('student.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A    B    C    D\n",
      "0  0.0  0.0  0.0  0.0\n",
      "1  0.0  0.0  0.0  0.0\n",
      "2  0.0  0.0  0.0  0.0\n",
      "     A    B    C    D\n",
      "0  1.0  1.0  1.0  1.0\n",
      "1  1.0  1.0  1.0  1.0\n",
      "2  1.0  1.0  1.0  1.0\n",
      "     A    B    C    D\n",
      "0  2.0  2.0  2.0  2.0\n",
      "1  2.0  2.0  2.0  2.0\n",
      "2  2.0  2.0  2.0  2.0\n",
      "     A    B    C    D\n",
      "0  0.0  0.0  0.0  0.0\n",
      "1  0.0  0.0  0.0  0.0\n",
      "2  0.0  0.0  0.0  0.0\n",
      "3  1.0  1.0  1.0  1.0\n",
      "4  1.0  1.0  1.0  1.0\n",
      "5  1.0  1.0  1.0  1.0\n",
      "6  2.0  2.0  2.0  2.0\n",
      "7  2.0  2.0  2.0  2.0\n",
      "8  2.0  2.0  2.0  2.0\n",
      "     A    B    C    D\n",
      "1  0.0  0.0  0.0  0.0\n",
      "2  0.0  0.0  0.0  0.0\n",
      "3  0.0  0.0  0.0  0.0\n",
      "     B    C    D    E\n",
      "2  1.0  1.0  1.0  1.0\n",
      "3  1.0  1.0  1.0  1.0\n",
      "4  1.0  1.0  1.0  1.0\n",
      "     B    C    D\n",
      "0  0.0  0.0  0.0\n",
      "1  0.0  0.0  0.0\n",
      "2  0.0  0.0  0.0\n",
      "3  1.0  1.0  1.0\n",
      "4  1.0  1.0  1.0\n",
      "5  1.0  1.0  1.0\n",
      "     A    B    C    D\n",
      "1  0.0  0.0  0.0  0.0\n",
      "2  0.0  0.0  0.0  0.0\n",
      "3  0.0  0.0  0.0  0.0\n",
      "     B    C    D    E\n",
      "2  1.0  1.0  1.0  1.0\n",
      "3  1.0  1.0  1.0  1.0\n",
      "4  1.0  1.0  1.0  1.0\n",
      "     A    B    C    D    B    C    D    E\n",
      "1  0.0  0.0  0.0  0.0  NaN  NaN  NaN  NaN\n",
      "2  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0\n",
      "3  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0\n",
      "     A    B    C    D\n",
      "0  0.0  0.0  0.0  0.0\n",
      "1  0.0  0.0  0.0  0.0\n",
      "2  0.0  0.0  0.0  0.0\n",
      "     A    B    C    D\n",
      "0  0.0  0.0  0.0  0.0\n",
      "1  0.0  0.0  0.0  0.0\n",
      "2  0.0  0.0  0.0  0.0\n",
      "3  1.0  1.0  1.0  1.0\n",
      "4  1.0  1.0  1.0  1.0\n",
      "5  1.0  1.0  1.0  1.0\n",
      "6  1.0  1.0  1.0  1.0\n",
      "7  1.0  1.0  1.0  1.0\n",
      "8  1.0  1.0  1.0  1.0\n",
      "     A    B    C    D\n",
      "0  0.0  0.0  0.0  0.0\n",
      "1  0.0  0.0  0.0  0.0\n",
      "2  0.0  0.0  0.0  0.0\n",
      "3  1.0  2.0  3.0  4.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#concatenating\n",
    "df1 = pd.DataFrame(np.ones((3,4))*0, columns=['A','B','C','D'])\n",
    "df2 = pd.DataFrame(np.ones((3,4))*1, columns=['A','B','C','D'])\n",
    "df3 = pd.DataFrame(np.ones((3,4))*2, columns=['A','B','C','D'])\n",
    "print(df1)\n",
    "print(df2)\n",
    "print(df3)\n",
    "res = pd.concat([df1,df2,df3], axis=0, ignore_index=True)\n",
    "print(res)\n",
    "#join['inner','outer']\n",
    "df1 = pd.DataFrame(np.ones((3,4))*0, columns=['A','B','C','D'], index=[1,2,3])\n",
    "df2 = pd.DataFrame(np.ones((3,4))*1, columns=['B','C','D','E'], index=[2,3,4])\n",
    "print(df1)\n",
    "print(df2)\n",
    "res = pd.concat([df1,df2],join='inner', ignore_index=True)\n",
    "print(res)\n",
    "df1 = pd.DataFrame(np.ones((3,4))*0, columns=['A','B','C','D'], index=[1,2,3])\n",
    "df2 = pd.DataFrame(np.ones((3,4))*1, columns=['B','C','D','E'], index=[2,3,4])\n",
    "print(df1)\n",
    "print(df2)\n",
    "res = pd.concat([df1,df2],axis=1,join_axes=[df1.index])\n",
    "print(res)\n",
    "df1 = pd.DataFrame(np.ones((3,4))*0, columns=['A','B','C','D'])\n",
    "df2 = pd.DataFrame(np.ones((3,4))*1, columns=['A','B','C','D'])\n",
    "df3 = pd.DataFrame(np.ones((3,4))*1, columns=['A','B','C','D'])\n",
    "print(df1)\n",
    "res = df1.append([df2,df3], ignore_index=True)\n",
    "print(res)\n",
    "df1 = pd.DataFrame(np.ones((3,4))*0, columns=['A','B','C','D'])\n",
    "s1=pd.Series([1,2,3,4],index=['A','B','C','D'])\n",
    "res = df1.append(s1,ignore_index=True)\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B key\n",
      "0  A0  B0  K1\n",
      "1  A1  B1  K2\n",
      "2  A2  B2  K3\n",
      "3  A3  B3  K4\n",
      "    C   D key\n",
      "0  C0  D0  K1\n",
      "1  C1  D1  K2\n",
      "2  C2  D2  K3\n",
      "3  C3  D3  K4\n",
      "    A   B key   C   D\n",
      "0  A0  B0  K1  C0  D0\n",
      "1  A1  B1  K2  C1  D1\n",
      "2  A2  B2  K3  C2  D2\n",
      "3  A3  B3  K4  C3  D3\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "left = pd.DataFrame({'key':['K1','K2','K3','K4'],\n",
    "                     'A':['A0','A1','A2','A3'],\n",
    "                     'B':['B0','B1','B2','B3']})\n",
    "right = pd.DataFrame({'key':['K1','K2','K3','K4'],\n",
    "                     'C':['C0','C1','C2','C3'],\n",
    "                     'D':['D0','D1','D2','D3']})\n",
    "print(left)\n",
    "print(right)\n",
    "res = pd.merge(left, right, on='key')\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B key1 key2\n",
      "0  A0  B0   K0   K0\n",
      "1  A1  B1   K0   K1\n",
      "2  A2  B2   K1   K0\n",
      "3  A3  B3   K2   K1\n",
      "    C   D key1 key2\n",
      "0  C0  D0   K0   K0\n",
      "1  C1  D1   K1   K0\n",
      "2  C2  D2   K1   K0\n",
      "3  C3  D3   K2   K0\n",
      "    A   B key1 key2   C   D\n",
      "0  A0  B0   K0   K0  C0  D0\n",
      "1  A2  B2   K1   K0  C1  D1\n",
      "2  A2  B2   K1   K0  C2  D2\n",
      "     A    B key1 key2    C    D\n",
      "0   A0   B0   K0   K0   C0   D0\n",
      "1   A1   B1   K0   K1  NaN  NaN\n",
      "2   A2   B2   K1   K0   C1   D1\n",
      "3   A2   B2   K1   K0   C2   D2\n",
      "4   A3   B3   K2   K1  NaN  NaN\n",
      "5  NaN  NaN   K2   K0   C3   D3\n",
      "    A   B key1 key2    C    D\n",
      "0  A0  B0   K0   K0   C0   D0\n",
      "1  A1  B1   K0   K1  NaN  NaN\n",
      "2  A2  B2   K1   K0   C1   D1\n",
      "3  A2  B2   K1   K0   C2   D2\n",
      "4  A3  B3   K2   K1  NaN  NaN\n",
      "     A    B key1 key2   C   D\n",
      "0   A0   B0   K0   K0  C0  D0\n",
      "1   A2   B2   K1   K0  C1  D1\n",
      "2   A2   B2   K1   K0  C2  D2\n",
      "3  NaN  NaN   K2   K0  C3  D3\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "left = pd.DataFrame({'key1':['K0','K0','K1','K2'],\n",
    "                     'key2':['K0','K1','K0','K1'],\n",
    "                     'A':['A0','A1','A2','A3'],\n",
    "                     'B':['B0','B1','B2','B3']})\n",
    "right = pd.DataFrame({'key1':['K0','K1','K1','K2'],\n",
    "                     'key2':['K0','K0','K0','K0'],\n",
    "                     'C':['C0','C1','C2','C3'],\n",
    "                     'D':['D0','D1','D2','D3']})\n",
    "print(left)\n",
    "print(right)\n",
    "res = pd.merge(left, right, on=['key1','key2'],how='inner')\n",
    "print(res)\n",
    "res = pd.merge(left, right, on=['key1','key2'],how='outer')\n",
    "print(res)\n",
    "res = pd.merge(left, right, on=['key1','key2'],how='left')\n",
    "print(res)\n",
    "res = pd.merge(left, right, on=['key1','key2'],how='right')\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   col1 col_left\n",
      "0     0        a\n",
      "1     1        b\n",
      "   col1  col_right\n",
      "0     1          2\n",
      "1     2          2\n",
      "2     2          2\n",
      "   col1 col_left  col_right      _merge\n",
      "0     0        a        NaN   left_only\n",
      "1     1        b        2.0        both\n",
      "2     2      NaN        2.0  right_only\n",
      "3     2      NaN        2.0  right_only\n",
      "   col1 col_left  col_right indicator_column\n",
      "0     0        a        NaN        left_only\n",
      "1     1        b        2.0             both\n",
      "2     2      NaN        2.0       right_only\n",
      "3     2      NaN        2.0       right_only\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']})\n",
    "print(df1)\n",
    "df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})\n",
    "print(df2)\n",
    "res = pd.merge(df1,df2,on='col1',how='outer',indicator=True)\n",
    "print(res)\n",
    "res = pd.merge(df1,df2,on='col1',how='outer',indicator='indicator_column')\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A   B\n",
      "K0  A0  B0\n",
      "K1  A1  B1\n",
      "K2  A2  B2\n",
      "     C   D\n",
      "K0  C0  D0\n",
      "K2  C2  D2\n",
      "K3  C3  D3\n",
      "      A    B    C    D\n",
      "K0   A0   B0   C0   D0\n",
      "K1   A1   B1  NaN  NaN\n",
      "K2   A2   B2   C2   D2\n",
      "K3  NaN  NaN   C3   D3\n",
      "     A   B   C   D\n",
      "K0  A0  B0  C0  D0\n",
      "K2  A2  B2  C2  D2\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "left = pd.DataFrame({'A':['A0','A1','A2'],\n",
    "                     'B':['B0','B1','B2']},\n",
    "                     index=['K0','K1','K2'])\n",
    "right = pd.DataFrame({'C':['C0','C2','C3'],\n",
    "                     'D':['D0','D2','D3']},\n",
    "                     index=['K0','K2','K3'])\n",
    "print(left)\n",
    "print(right)\n",
    "res = pd.merge(left,right,left_index=True,right_index=True,how='outer')\n",
    "print(res)\n",
    "res = pd.merge(left,right,left_index=True,right_index=True,how='inner')\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age_boy   k  age_girl\n",
      "0        1  K0         4\n",
      "1        1  K0         5\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "boys = pd.DataFrame({'k':['K0','K1','K2'],'age':[1,2,3]})\n",
    "girls = pd.DataFrame({'k':['K0','K0','K3'],'age':[4,5,6]})\n",
    "res = pd.merge(boys,girls,on='k',suffixes=['_boy','_girl'],how='inner')\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hh9Bgs6O17nLdL1/fxoTfr+TtrUfdjlc0tFjbP3xhM898edjar25sdbv29le+5rQHPqag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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2714d407240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = pd.Series(np.random.randn(1000),index=np.arange(1000))\n",
    "data = data.cumsum()\n",
    "data.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jPY/Muc3jUpKG4cBybdseRAvrVPc5Ex4XBsMvFM65T+zb8Jr7Y/cthefCRRB7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2714d41c7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = pd.DataFrame(np.random.randn(1000,4),\n",
    "                index = np.arange(1000),\n",
    "                columns=list(\"ABCD\"))\n",
    "data=data.cumsum()\n",
    "data.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2714d41c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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H85CXl9LmMSjn+O1vf6K698KtOJTzBge8u1IC6Fxhu546EUVmPDs7G6+++ioA4MCBA1GX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\nKBQKRUb+Hyk0tqTcjluyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2714d715860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = pd.DataFrame(np.random.randn(1000,4),\n",
    "                index = np.arange(1000),\n",
    "                columns=list(\"ABCD\"))\n",
    "data=data.cumsum()\n",
    "ax = data.plot.scatter(x='A',y='B',color='DarkBlue',label='Class1')\n",
    "data.plot.scatter(x='A',y='C',color='DarkGreen',label='Class2',ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EA/bneMIPGxn/+WvUyjxmaPP6VbexuoFr5BkrjlZAjoiBxBlkB+BnvDXTXLz5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\nqW9yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2634aa73278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数据可视化\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def add_layer(inputs, in_size, out_size, activation_function=None):\n",
    "    Weights = tf.Variable(tf.random_normal([in_size,out_size]))\n",
    "    biases = tf.Variable(tf.zeros([1, out_size])) + 0.1 #biases初始值不为零\n",
    "    Wx_plus_b = tf.matmul(inputs, Weights) + biases\n",
    "    if activation_function is None:\n",
    "        outputs = Wx_plus_b\n",
    "    else:\n",
    "        outputs = activation_function(Wx_plus_b)\n",
    "    return outputs\n",
    "\n",
    "x_data = np.linspace(-1,1,300)[:,np.newaxis]\n",
    "noise = np.random.normal(0,0.05,x_data.shape).astype(np.float32)#添加噪点，尽量的让数据不那么完美，符合数据的特点\n",
    "y_data = np.square(x_data)-0.5+noise\n",
    "\n",
    "xs = tf.placeholder(tf.float32, [None, 1])\n",
    "ys = tf.placeholder(tf.float32, [None, 1])\n",
    "l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)#输入层\n",
    "prediction = add_layer(l1, 10, 1, activation_function=None)\n",
    "\n",
    "loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),\n",
    "                     reduction_indices=[1]))\n",
    "train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.scatter(x_data, y_data)\n",
    "plt.ion()\n",
    "plt.draw()\n",
    "for i in range(1000):\n",
    "    sess.run(train_step, feed_dict={xs:x_data,ys:y_data})\n",
    "    if i%50 ==0:\n",
    "        \n",
    "    # print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))\n",
    "        try:\n",
    "            ax.lines.remove(lines[0])\n",
    "        except Exception:\n",
    "            pass\n",
    "\n",
    "        prediction_value = sess.run(prediction, feed_dict={xs:x_data})\n",
    "        lines = ax.plot(x_data,prediction_value,'r-',lw=5)\n",
    "        plt.pause(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
